Predicting Conflict in Space and Time
Bibliographic Data
| ID | 6284380 |
|---|---|
| Authors | Nils B Weidmann (0000-0002-4791-4913, Princeton University, corresponding author), Michael D Ward (0000-0002-6561-6186, Duke University) |
| Year | 2010 |
| Volume | 54 |
| Issue | 6 |
| Pages | 883-901 |
| Publication date | 2010-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Conflict Resolution (JOURNAL) |
| Journal identifiers | ISSN: 0022-0027 • E-ISSN: 1552-8766 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/0022002710371669 |
| OpenAlex | W2170121971 |
| Language | EN |
| Citations received | 72 |
| References cited | 20 |
The prediction of conflict constitutes a challenge to social scientists. This article explores whether the incorporation of geography can help us make our forecasts of political violence more accurate. The authors describe a spatially and temporally autoregressive discrete regression model, following the framework of Geyer and Thompson. This model is applied to geo-located data on attributes and conflict events in Bosnia over the period from March 1992 to October 1995. Results show that there is a strong spatial as well as temporal dimension to the outbreak of violence in Bosnia. The authors then explore the use of this model for predicting future conflict. Using a simulation approach, the predictive accuracy of the spatial—temporal model is compared to a standard regression model that only includes time lags. The results show that even in a difficult out-of-sample prediction task, the incorporation of space improves our forecasts of future conflict
Autoregressive model · Dimension (graph theory · Econometrics · Geography · Regression · Regression analysis · Space (punctuation) · Statistics · Agricultural risk and resilience · Computer Science · Conservation, Biodiversity, and Resource Management · Crime Patterns and Interventions · Mathematics
Forecasting in International Relations
Varying climatic-social-geographical patterns shape the conflict risk at regional and global scales
Spatiotemporal evolution of Nigeria’s armed conflicts and terrorism and the associated shift in social perceptions
Michael D. Ward (1948–2021) and the road to space, networks and geography
The Promise and Pitfalls of Conflict Prediction
Violence and Cell Phone Communication
Signalling and Balancing in the Conflict in Ukraine from 2014 to 2016
Neural Networks and Political Science
Drought impacts on armed conflict primarily explained by pre-existing conflict risk
Modeling Strategic Decisions in the Formation of the Early Neo-Assyrian Empire
Quantum Mechanisms
Can We Predict Politics? Toward What End
Using machine-coded event data for the micro-level study of political violence
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Forecasting Civil Conflict with Zero-Inflated Count Models
Polarisation, accountability, and interstate conflict
Modelling Violence as Disease? Exploring the Possibilities of Epidemiological Analysis for Peacekeeping Data in Darfur
World Heritage in danger
Text as data for crisis-early warning
Micro-cleavages and violence in civil wars
Introducing SpatialGridBuilder
The timing of conflict violence
Forecasting conflict in Africa with automated machine learning systems
Event Data on Armed Conflict and Security
Race to the bottom
Forecasting change in conflict fatalities with dynamic elastic net
High resolution conflict forecasting with spatial convolutions and long short-term memory
Is Theory Useful for Conflict Prediction? A Response to Beger, Morgan, and Ward
Neighbouring an insurgency
Precision-guided or blunt? The effects of US economic sanctions on human rights
Blessing or curse? Assessing the local impacts of foreign direct investment on conflict in Africa
Forecasting Peace Agreement Content
Spikes and Variance
Technology and Collective Action
Actor Fragmentation and Civil War Bargaining
Who Wants Peace? Predicting Civilian Preferences in Conflict Negotiations
Deduplication of the media-based event databases
Regions at Risk
Long-Term Effects in Models with Temporal Dependence
Human Rights Violations in Space
Introducing an Interpretable Deep Learning Approach to Domain-Specific Dictionary Creation
Crowdseeding in Eastern Congo
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On the Accuracy of Media-based Conflict Event Data
Predicting Armed Conflict, 2010-20501
Terrorist Group Cooperation and Longevity
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Roving Bandits? The Geographical Evolution of African Armed Conflicts
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A Closer Look at Reporting Bias in Conflict Event Data
Mere Description
How ethnicity conditions the effect of oil and gas on civil conflict
Inherently vulnerable? Ethnic geography and the intensity of violence in the Bosnian civil war
Roads and the diffusion of insurgent violence
Diffusion patterns of violence in civil wars
Protecting the capital? On African geographies of protest escalation and repression
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The Diffusion of Violence in the North Caucasus of Russia, 1999-2010
Can civilian attitudes predict insurgent violence? Ideology and insurgent tactical choice in civil war
The geo-temporal evolution of violence in civil conflicts
Denial and punishment in the North Caucasus
Prio-Grid
Capital punishment
Early warning signals for war in the news
ViEWS
Seven deadly sins of contemporary quantitative political analysis
Mountainous Terrain and Civil Wars
Timing Is Everything
Violent conflict and the demand for healthcare
Reading Between the Lines
The Spillover Effect of the Syrian Crisis on the Peace Process in Turkey
Spatial Interaction and the Statistical Analysis of Lattice Systems
A predictive approach to the random effect model
World handbook of political and social indicators
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Contagion or Confusion? Why Conflicts Cluster in Space
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Introducing Acled
Ethnicity, Insurgency, and Civil War
| Unique citing works | 72 |
|---|---|
| Citations per year | 4,8 |
| Citation span | 2011 - 2026 (16) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 72 |